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Transportation networks play an important role in urban areas, and bridges are the most vulnerable structures to earthquakes. The seismic damage evaluation of bridges provides an effective tool to assess the potential damage, and guides the post-earthquake recovery operations. With the help of structural health monitoring (SHM) techniques, the structural condition could be accurately evaluated through continuous monitoring of structural responses, and evaluating vibration-based features, which could reflect the deterioration of materials and boundary conditions, and are extensively used to reflect the structural conditions. This study proposes a vibration-based seismic damage state evaluation method for regional bridges. The proposed method contains the measured structural dynamic parameters and bridge configuration parameters. In addition, several intensity measures are also included in the model, to represent the different characteristics and the regional diversity of ground motions. The prediction models are trained with a random forest algorithm, and their confusion matrices and receiver operation curves reveal a good prediction performance, with over 90% accuracy. The significant parameter identification of bridge systems and components reveals the critical parameters for seismic design, disaster prevention and structure retrofit.

Vibration-Based Seismic Damage States Evaluation for Regional Concrete Beam Bridges Using Random Forest Method